Identification and resolution of conflicts during ontological integration using rules

Abstract: Integration of ontologies of information sources and consumers is an important phase in achieving web‐based interoperability. The present work describes an approach for identifying certain semantic conflicts while integrating ontologies of heterogeneous information sources. This paper is focused on the identification of homonymy and synonymy between elements in ontologies. In the present work the concepts of homonymy and synonymy are synonymous to naming conflicts and entity identifier conflicts, respectively, and partial synonymy is synonymous to schema isomorphism conflicts. The concept of the mask of interoperability is introduced for the identification of synonymy. The mask of interoperability is expressed in a declarative way as a set of rules, which can then be used for resolution of conflicts during integration of ontologies. As proof of concept, ontologies are implemented using the XML‐based ontology language Ontology Web Language (OWL), and the rules are implemented using the emerging rule language Semantic Web Rule Language (SWRL). This representation in OWL and SWRL allows the ontology to be executable, flexibly extendable and platform‐independent. The OWL facts and SWRL rules are used by the Jess and Bossam reasoning engine to identify semantic homonymy and synonymy.     

《数据结构》课程知识本体构建研究

介绍当代移动学习的特点和语义网本体技术,提出利用本体技术来适应移动学习的片段式学习方式要求,并以《数据结构》课程为例阐述课程知识本体库的具体构建过程．为其在移动学习系统中的应用打下研究基础。     

基于数据场的大规模本体映射

针对已有的本体映射方法在处理大规模本体映射任务时效率和有效性较低的问题,文中提出了一个基于数据场的本体映射算法.该算法首先使用高效的相似度算法,建立本体中元素对另一本体的初始相关度;然后,利用数据场势函数引入周围本体元素对当前元素的影响,修正初始相关度,并最终确定本体间的相关子本体;最后,利用针对性的方法对上述相关子本体进行更有效的映射.实验结果表明,该算法可以在提高映射结果质量的同时保证较高的映射效率.     

A SKOS-based framework for Subject Ontologies to improve learning experiences

Subject Ontologies represent conceptualizations of disciplinary domains in which concepts symbolize topics that are relevant for the considered domain and are associated each other by means of specific relations. Usually, these kind of lightweight ontologies are adopted in knowledge-based educational environments to enable semantic organization and search of resources and, in other cases, to support personalization and adaptation features for learning and teaching experiences. For this reason, applying effective management methodologies for Subject Ontologies is a crucial aspect in engineering the environments. In particular, this paper proposes an approach to use SKOS (a Semantic Web-based vocabulary providing a standard way to represent knowledge organization systems) for modelling subject ontologies. Moreover, the paper underlines the main benefits of SKOS. It focuses on alternative strategies for storing and accessing ontologies in order to support the knowledge sharing, knowledge reusing, planning, assessment, customization and adaptation processes related to learning scenarios. The results of an early experimentation allowed the authors defining a framework able to support, from both methodological and technological viewpoints, the use of Subject Ontologies in the context of a Semantic Web-based Educational System. The defined framework has high performances in terms of response and this may really improve the user experience.     

An adaptive ontology mapping approach with neural network based constraint satisfaction

Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. It is a key challenge to achieve semantic interoperability in building the Semantic Web. This paper proposes a new generic and adaptive ontology mapping approach, called the PRIOR+, based on propagation theory, information retrieval techniques and artificial intelligence. The approach consists of three major modules, i.e., the IR-based similarity generator, the adaptive similarity filter and weighted similarity aggregator, and the neural network based constraint satisfaction solver. The approach first measures both linguistic and structural similarity of ontologies in a vector space model, and then aggregates them using an adaptive method based on their harmonies, which is defined as an estimator of performance of similarity. Finally to improve mapping accuracy the interactive activation and competition neural network is activated, if necessary, to search for a solution that can satisfy ontology constraints. The experimental results show that harmony is a good estimator of f-measure; the harmony based adaptive aggregation outperforms other aggregation methods; neural network approach significantly boosts the performance in most cases. Our approach is competitive with top-ranked systems on benchmark tests at OAEI campaign 2007, and performs the best on real cases in OAEI benchmark tests.     

语义集成中的不确定性本体映射模型

语义Web的高速发展使其具有动态性和异构性特征,解决语义信息的异构性问题成为实现信息集成的关键。本体作为一种语义Web的知识表示形式,增强了Web的语义信息。因此,为了解决语义异构性,实现数据间的互操作,必须建立异构本体间的映射关系。然而,为庞大的异构本体建立完全精确的本体映射是不现实的,本体映射中存在一定的不确定性。提出了一种新型的本体映射框架——语义集成中的不确定性本体映射。从不同方面研究本体特征,集合了多种映射策略,并引入了各映射策略中不确定性匹配的解决方案。实验证明,该方法具有可靠的实验性能,并且具有很好的通用性和可扩展性。     

基于奇异值分解的中文Ontology自动学习技术

语义Web是一个美好的构想，Ontology在语义Web中起着举足轻重的作用，它不仅能为人类用户而且能为软件agent提供从语法层次到语义层次上的互操作性。目前Web上主要是各种布局的HTML文档，未来的语义Web页面将是各种领域Ontology的实例以及到其它实例上的链接，因此语义Web的成功强烈依赖于Ontology的增殖，方便快捷地构造各领城Ontology是实现语义Web的关健。该文提出一种基于奇异值分解的中文Ontology自动学习技术，这种技术的特点是其简易性以及准确的数学理论基础。     

利用Prolog实现语义网中的知识推理

元数据的交换是实现语义网应用的基础。在语义网的架构中,Ontology语言利用自己的元级描述能力来建立元数据之间的联系,从而实现语义的交换。本文分析了DAML OIL语言的知识描述能力,并对它蕴含的关于类的知识建立了Prolog的推理规则,最后结合XSBProlog推理引擎和InterProlog接口用Java语言实现了对Ontology的推理,从而实现了不同Ontology之间的元数据交换。     

An XML query rewriting mechanism with multiple ontologies integration based on complex semantic mapping

Semantic integration, which can be divided into three parts including ontology mapping, mapping representation, and reasoning and query rewriting with mappings, plays a key role in information integration systems. This paper develops an XML query rewriting and ontology integration mechanism, which acts as a global-as-view (GAV) approach to represent and query semantic information in mediator based information integration environment. It proposes the patterns and properties of ontology mappings, discusses the procedure and algorithm of ontology integration firstly, and then proposes the ontology based XML query mechanism, especially the XML query rewriting mechanism. Finally, a mediator-based implementation of the mechanism in OBSA system is introduced.     

A semantic interoperability approach to health‐care data: Resolving data‐level conflicts

The integration of data from various electronic health record (EHR) systems presents a critical conflict in the sharing and exchanging of patient information across a diverse group of health‐oriented organizations. Patient health records in each system are annotated with ontologies utilizing different health‐care standards, creating ontology conflicts both at the schema and data levels. In this study, we introduce the concept of semantic ontology mapping for the facilitation and interoperability of heterogeneous EHR systems. This approach proposes a means of detecting and resolving the data‐level conflicts that generally exist in the ontology mapping process. We have extended the semantic bridge ontology in support of ontology mapping at the data level and generated the required mapping rules to reconcile data from different ontological sources into a canonical format. As a result, linked‐patient data are generated and made available in a semantic query engine to facilitate user queries of patient data across heterogeneous EHR systems.     

本体相似度研究

不同本体之间的交互成为语义Web的首要任务,其中本体相似度计算是本体映射的关健环节。在以往的研究中,本体相似度计算通常专注于模式及其结构的匹配。目前研究朝着进一步考虑本体内部语义信息方向努力。本文描述了语义相似度栈的各个层次,依据各个层次的语义特征对目前本体相似度方法进行分类,并对每种方法进行了详细描述。最后对现有一些主要的本体间相似度计算方法进行归纳总结。这项研究工作将为人们提出新的相似度方法或者组合的计算方法作一个参考。     

Learning to match ontologies on the Semantic Web

On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible on the Web scale. Hence the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies and describe experiments that show the promise of the approach.Received: 16 December 2002, Accepted: 16 April 2003, Published online: 17 September 2003Edited by: Edited by B.V. Atluri, A. Joshi, and Y. Yesha     

Onyx: A new Canvas-based tool for visualizing biomedical and health ontologies

Ontologies provide formal, machine-readable, and human-interpretable representations of domain knowledge. Therefore, ontologies have come into question with the development of Semantic Web technologies. People who want to use ontologies need an understanding of the ontology, but this understanding is very difficult to attain if the ontology user lacks the background knowledge necessary to comprehend the ontology or if the ontology is very large. Thus, software tools that facilitate the understanding of ontologies are needed. Ontology visualization is an important research area because visualization can help in the development, exploration, verification, and comprehension of ontologies. This paper introduces the design of a new ontology visualization tool, which differs from traditional visualization tools by providing important metrics and analytics about ontology concepts and warning the ontology developer about potential ontology design errors. The tool, called Onyx, also has advantages in terms of speed and readability. Thus, Onyx offers a suitable environment for the representation of large ontologies, especially those used in biomedical and health information systems and those that contain many terms. It is clear that these additional functionalities will increase the value of traditional ontology visualization tools during ontology exploration and evaluation.     

Semantic oriented ontology cohesion metrics for ontology-based systems

Ontologies play a core role to provide shared knowledge models to semantic-driven applications targeted by Semantic Web. Ontology metrics become an important area because they can help ontology engineers to assess ontology and better control project management and development of ontology based systems, and therefore reduce the risk of project failures. In this paper, we propose a set of ontology cohesion metrics which focuses on measuring (possibly inconsistent) ontologies in the context of dynamic and changing Web. They are: Number of Ontology Partitions (NOP), Number of Minimally Inconsistent Subsets (NMIS) and Average Value of Axiom Inconsistencies (AVAI). These ontology metrics are used to measure ontological semantics rather than ontological structure. They are theoretically validated for ensuring their theoretical soundness, and further empirically validated by a standard test set of debugging ontologies. The related algorithms to compute these ontology metrics also are discussed. These metrics proposed in this paper can be used as a very useful complementarity of existing ontology cohesion metrics.     

Ontology-based curriculum content sequencing system with semantic rules

Curriculum content sequencing involves managing a learning route to help users achieve learning goals. A conventional learning route consists of ordered content and is primarily based on a single course material. In an e-learning system, amount of similar course contents are available. These contents are expected to mutually substitute for one another in creating flexible learning routes. Owing to inconsistency in materials editing and cataloging, composing contents based on multiple sources leads to sequencing complexity. Most significantly, most e-learning systems lack a sequencing mechanism for dominating content composition. This study utilizes a knowledge-intensive approach to create a general sequencing knowledge base. This approach includes two components: (1) ontology is used to represent abstract views of content sequencing and course materials and (2) added semantic rules are used to represent relationships between individuals. Following knowledge base creation, both practical curriculum sequences and course materials can be inserted as factual knowledge. A reliable knowledge base can be established using inference power. An example involving mathematics course in elementary school education is designed using Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). Experimental lessons demonstrate that semantic rules in conjunction with ontologies not only solve sequencing problems but also achieve a durable knowledge base and a reliable system.     

基于语义分析的OWL文档聚类研究

借助目前丰富的网络资源，将同一主题的现存Ontology知识聚类，提供给领域专家或用户进行二次精化和集成是Ontology研究领域的一个重要课题．OWL是目前用于表示和交换Ontology信息的基本标准．本文从OWL的语义本质出发，考虑了知识之间的继承性及复杂类比较和模糊集运算的相似性，提出一种计算OWL文档语义相似性的方式，并和层次聚类算法集成完成了对OWL文档集的聚类实验．实验结果说明本文提出的算法对自动生成和手工建立的OWL文档集都有很好的效果。     

An ontology-based framework to support intelligent data analysis of sensor measurements

In the past years, the large availability of sensed data highlighted the need of computer-aided systems that perform intelligent data analysis (IDA) over the obtained data streams. Temporal abstractions (TAs) are key to interpret the principle encoded within the data, but their usefulness depends on an efficient management of domain knowledge. In this article, an ontology-based framework for IDA is presented. It is based on a knowledge model composed by two existing ontologies (Semantic Sensor Network ontology (SSN), SWRL Temporal Ontology (SWRLTO)) and a new developed one: the Temporal Abstractions Ontology (TAO). SSN conceptualizes sensor measurements, thus enabling a full integration with semantic sensor web (SSW) technologies. SWRLTO provides temporal modeling and reasoning. TAO has been designed to capture the semantic of TAs. These ontologies have been aligned through DOLCE Ultra-Lite (DUL) upper ontology, boosting the integration with other domains. The resulting knowledge model has a modular design that facilitates the integration, exchange and reuse of its constitutive parts. The framework is sketched in a chemical plant case study. It is shown how complex temporal patterns that combine several variables and representation schemes can be used to infer process states and/or conditions.     

从ER模式到OWL DL本体的语义保持的翻译

提出了一种从ER模式到OWL DL本体的语义保持的翻译方法.该方法在形式化表示ER模式的基础上,建立ER模式和OWL DL本体之间精确的概念对应,通过一个翻译算法按照一组预定义的映射规则实现模式翻译.理论分析表明,该方法是语义保持的和有效的;算法实现和案例研究进一步证实,完全自动的机器翻译是可实现的.该文方法是原创性的,为Web本体的开发以及数据库和语义Web之间语义互操作的实现开辟了一条有效途径.     

中文本体映射研究与实现

本体间的异构是语义网建设亟待解决的问题,本体映射则是解决本体异构的有效手段。中文资源是信息网络的重要组成部分,实现中文本体间以及中文与其他本体的映射是实现知识共享重用的一个重要组成部分。本文从元素层的角度对中文本体映射进行了研究,提出利用知网,结合多种技术计算词汇相似度,利用词汇的相似度计算概念匹配的可信度,实现元素层本体映射的算法,并根据此算法实现了ELOMC(Element Level Ontology Matching for Chinese)系统。